Weather forecasting, cloud movement prediction and gaseous contaminations distribution from a localized source are most commonly acquired using satellite data. In existing approaches, the spatial resolutions of the acquired data are of the orders of tens of miles and data localization on a map grid can be shifted from the real geographical locations. Complex models commonly take the high spatial resolution satellite data and combine it with first principle modeling to achieve a local prediction. Based on physical models the weather, solar radiation or contamination is estimated and predicted locally and projected over the long term.
Satellite based models may work on the time scales of hours up to days but would be highly inaccurate for prediction on short term time scale such as minutes. In such cases, local measurements and predictive models are developed for attempted accuracy of short term predictions. Multiple forecasting methods have to be employed to extend the prediction from seconds up to days. The forecasts rely on different information and the degree of physical information that is embedded in such a model changes from one method to another. Forecasting at a timescale, from seconds up to days in advance, the long term (satellite) and short term (local sensors) measurement and prediction has to be combined. To bridge the gap between different data sets and establish a smooth transition from prediction based on different data sets requires physical models to establish the coupling parameters between the two observational models.
Distributed sensor networks are commonly encountered today on large scale geographical areas, such as, for example, solar panels mounted nearby roads and highways systems used for monitoring traffic, solar panels on the roofs of houses that are distributed over large geographical areas, air quality measurement by government agencies that monitor sets of parameters in many cities across the world, satellite based observations or mobile sensor networks such as sensors that are integrated in cars or cell phones such as light sensors for turning on headlights at dusk or locations based on global positioning system (GPS) signals.
Local sensors that can be employed for short term forecasting include, for example, sky cameras to observe cloud movement, pyronometers to measure solar radiation, and corrosion sensors to predict the gaseous contamination of the atmosphere.